Intro to Data Viz

Jim Rose

Lecture for IBS519, Fall 2023

Who am I?

One of your TAs for this course
PhD student (5th year, Genetics & Molecular Biology program)
Computational immunology/gene regulation research
Self-taught coder (R & python)

My website (build one for yourself!):

Main Q’s for Today:

Why look at (visualize) data?

How should I look at data?

What the heck is ggplot2? And how do I use it?

Part 1: Why visualize data?

To explore or understand data better

To convey a point (or make an argument)

Visualizing to explore data

Sometimes visualzing data can tell you more than summary statistics…

Visualizing to convince or make a point

When data visualization goes wrong

Part 2: How should I visualize data?

“With great power comes great responsibility” – Uncle Ben Parker

How should I visualize data?

Pillar 1: Show the data (honestly)

Pillar 2: Think about perception

You need to think about the WAY your audience’s BRAIN will PERCEIVE your chart
Perceptual accuracy is influenced by the way you are conveying information.

(i.e. type of chart used)

Think about your plot in terms of “channels”

Pillar 3: Reduce Clutter

Code

library(tidyverse)

poke <- read_csv("./DataSets/pokemon.csv")

ggplot(poke, aes(x=Attack, y=Defense)) + 
  geom_point() +
  geom_text(aes(label=Name))

Pillar 3: Reduce Clutter

Code

library(tidyverse)
library(ggrepel)

poke <- read_csv("./DataSets/pokemon.csv")

ggplot(data=poke, aes(x=Attack, y=Defense)) + 
  geom_point(alpha=0.8, color='darkgrey') + geom_text_repel(aes(label=ifelse(Defense>125 | Attack >140, as.character(Name), ""))) + 
  geom_density2d()

Pillar 4: Use color strategically

To indicate groups or categories

Pillar 4: Use color strategically

To highlight

DON’T MAKE RAINBOW CLOWN VOMIT CHARTS

Consider inclusivity when choosing a color palette

Pillar 5: Get creative

W.E.B. Du Bois

Paris Exhibition 1900

Consult a chart library or atlas for ideas on chart types

https://r-graph-gallery.com/

Other great resources

Data Visualization with R by Rob Kabacoff, PhD

Better Data Visualizations by Jonathan Schwabish

Data Visualization: A Practical Introduction by Kieran Healy